A Comparison of Software Fault Imputation Procedures

Authors:
Jason Van Hulse;Taghi M. Khoshgoftaar;Chris Seiffert
Affiliations:
Florida Atlantic University, USA;Florida Atlantic University, USA;Florida Atlantic University, USA
Venue:
ICMLA '06 Proceedings of the 5th International Conference on Machine Learning and Applications
Year:
2006

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Incomplete-case nearest neighbor imputation in software measurement data

Information Sciences: an International Journal

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Abstract

This work presents a detailed comparison of three imputation techniques, Bayesian multiple imputation, regression imputation and k nearest neighbor imputation, at various missingness levels. Starting with a complete real-world software measurement dataset called CCCS, missing values were injected into the dependent variable at four levels according to three different missingness mechanisms. The three imputation techniques are evaluated by comparing the imputed and actual values. Our analysis includes a three-way analysis of variance (ANOVA) model, which demonstrates that Bayesian multiple imputation obtains the best performance, followed closely by regression.